Cutting corners on context could be costing enterprises from reaching the full potential of their AI agent deployments.
Neglecting semantics will cause AI agents to be inaccurate and inefficient, exposing organisations to wasted spending and increased data and AI governance vulnerabilities, said Gartner, Inc.
AI agents need to understand the context inputs in each step of the agentic workflow to deliver accurate responses at an optimal cost.
“Agentic AI outcomes depend on context including semantic representations of data,” Rita Sallam, Distinguished VP Analyst at Gartner, said.
“Without context – a clear understanding of the specific relationships and rules within an organisation’s data – AI agents cannot operate accurately and are far more likely to hallucinate, introduce bias and produce unreliable results.
“Organisations that fail to adopt comprehensive context structures — supported by a robust data layer — will perpetuate data inefficiencies and face heightened financial costs, as well as legal and reputational damage.”
Gartner predicts that by 2027, organizations that prioritize semantics in AI-ready data will increase their agentic AI accuracy by up to 80% and reduce costs by up to 60%.
Gartner advises data and analytics (D&A) leaders to establish a context layer as a core component of their D&A infrastructure. Traditional schema-based data models alone no longer suffice for agentic AI because they lack business context and data meaning.
Recommended reading
Gartner expects that regulators will demand greater semantic transparency, and boards will increasingly treat semantic governance as both a strategic risk and a competitive opportunity.
“Context with semantic coherence will become a cost-control and trust strategy, not a nice-to-have,” said Sallam. “By reducing errors and increasing trust, semantics will push organizations to budget for semantic capabilities as a non-negotiable foundation.”
Related